Piecewise Strong Convexity of Neural Networks

NeurIPS 2019 Tristan Milne

We study the loss surface of a feed-forward neural network with ReLU non-linearities, regularized with weight decay. We show that the regularized loss function is piecewise strongly convex on an important open set which contains, under some conditions, all of its global minimizers... (read more)

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